Mixed-methods research: What's in it for economists?

被引:12
|
作者
Jefferson, Therese [1 ]
Austen, Siobhan [2 ,3 ]
Sharp, Rhonda [4 ]
Ong, Rachel [2 ,3 ]
Lewin, Gill [5 ]
Adams, Valerie [4 ]
机构
[1] Curtin Univ, Perth, WA 6845, Australia
[2] Curtin Univ, Ctr Res Appl Econ, Perth, WA 6845, Australia
[3] Curtin Univ, Sch Econ & Finance, Perth, WA 6845, Australia
[4] Univ S Australia, Hawke Inst, Adelaide, SA 5001, Australia
[5] Curtin Univ, Ctr Res Ageing, Perth, WA 6845, Australia
来源
ECONOMIC AND LABOUR RELATIONS REVIEW | 2014年 / 25卷 / 02期
基金
澳大利亚研究理事会;
关键词
Employment conditions; gender; low-paid workers; mixed methods; research methodology; FEMINIST ECONOMICS; CRITICAL REALISM;
D O I
10.1177/1035304614530819
中图分类号
F [经济];
学科分类号
02 ;
摘要
Empirical studies in economics traditionally use a limited range of methods, usually based on particular types of regression analysis. Increasingly, sophisticated regression techniques require the availability of appropriate data sets, often longitudinal and typically collected at a national level. This raises challenges for researchers seeking to investigate issues requiring data that are not typically included in regular large-scale data. It also raises questions of the adequacy of relying mainly or solely on regression analysis for investigating key issues of economic theory and policy. One way of addressing these issues is to employ a mixed-methods research framework to investigate important research questions. In this article, we provide an example of applying a mixed-methods design to investigate the employment decisions of mature age women working in the aged care sector. We outline the use of a coherent and robust framework to allow the integrated collection and analysis of quantitative and qualitative data. Drawing on particular examples from our analysis, we show how a mixed-methods approach facilitates richer insights, more finely grained understandings of causal relationships and identification of emergent issues. We conclude that mixed-methods research has the capacity to provide surprises and generate new insights through detailed exploratory data analysis.
引用
收藏
页码:290 / 305
页数:16
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